Conventional Clinicopathological Features Research Articles

Identification and validation of mitochondria-related LncRNA signatures as a novel prognostic model for glioma.

A predictive model for long-term survival is needed, and mitochondrial dysfunction is a key feature of cancer metabolism, though its link to glioma is not well understood. The aim of this study was to identify the molecular characteristics associated with glioma prognosis and explore its potential function. We analyzed RNA-seq data from The Cancer Genome Atlas and identified differentially expressed mitochondrial long noncoding RNAs (lncRNAs) using R's 'limma' package. A prognostic model was developed using 10 selected lncRNAs and validated with Cox regression and least absolute shrinkage and selection operator algorithm. The model's efficacy was assessed using Kaplan-Meier and receiver operating characteristic curve analyses, and its correlation with immune cell profiles and drug sensitivity was explored. A 10-mitochondria-related LncRNA signature was generated. The median risk score values are used to classify glioma samples into low-risk and high-risk groups. In breast patients, the signature-based risk score demonstrated a more potent ability to predict survival than conventional clinicopathological features. Furthermore, we noted a substantial disparity in the number of immune cells, including B cells, CD8+T cells, and macrophages, between the two groups. In addition, the high-risk group exhibited lower half-maximal inhibitory concentration values for specific chemotherapy medications, including bortezomib, luminespib, rapamycin, and 5-fluorouracil. Our study elucidates the diagnostic and prognostic value of mitochondria-related-lncRNAs in the promotion, suppression, and treatment of glioma.

Adding contrast-enhanced ultrasound can improve the predictive ability of breast conventional ultrasound and mammography for pathological upgrade of biopsy-confirmed ductal carcinoma in situ

ObjectivesTo evaluate the added value of contrast-enhanced ultrasound (CEUS) on top of breast conventional imaging for predicting the upgrading of ductal carcinoma in situ (DCIS) to invasive cancer after surgery. MethodsThis retrospective study enrolled 140 biopsy-proven DCIS lesions in 138 patients and divided them into two groups based on postoperative histopathology: non-upgrade and upgrade groups. Conventional ultrasound (US), mammography (MMG), CEUS and clinicopathological (CL) features were reviewed and compared between the two groups. The predictive performance of different models (with and without CEUS features) for histologic upgrade were compared to calculate the added value of CEUS. ResultsFifty-nine (42.1 %) lesions were histologically upgraded to invasive cancer after surgery. By logistic regression analyses, we found that high-grade DCIS at biopsy (P=0.004), ultrasonographic lesion size > 20 mm (P=0.007), mass-like lesion on US (P=0.030), the presence of suspicious calcification on MMG (P=0.014), the presence of perfusion defect (P=0.005) and the area under TIC>1021.34 ml (P<0.001) on CEUS were six independent factors predicting concomitant invasive components after surgery. The CL+US+MMG model made with the four predictors in the clinicopathologic, US and MMG categories yielded an area under the receiver operating curve (AUROC) value of 0.759 (95 % CI: 0.680–0.828) in predicting histological upgrade. The combination model built by adding the two CEUS predictors to the CL+US+MMG model showed higher predictive efficacy than the CL+US+MMG model (P=0.018), as the AUROC value was improved to 0.861 (95 % CI: 0.793–0.914). ConclusionsThe addition of contrast-enhanced ultrasound to breast conventional imaging could improve the preoperative prediction of an upgrade to invasive cancer from CNB −proven DCIS lesions.

A novel gene signature related to fatty acid metabolism predicts prognosis, immune landscape, and drug sensitivity in early-stage lung squamous cell carcinoma.

Dysregulation of fatty acid metabolism (FAM) represents a significant metabolic alteration in tumorigenesis. However, the role of FAM-related genes (FAMRGs) in early-stage lung squamous cell carcinoma (LUSC) remains incompletely understood. A series of bioinformatic analyses and machine learning strategies were performed to construct a FAMRGs-based signature to predict prognosis and guide personalized treatment for early-stage LUSC patients. FAMRGs were screened through the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Molecular Signature Database (MSigDB). Prognosis FAMRGs were identified using univariate Cox regression, and unsupervised clustering analysis facilitated the division of the cohort into different clusters. The least absolute shrinkage and selection operator (LASSO)-Cox regression and multivariate regression analysis were employed to develop a FAMRGs-based signature for predicting overall survival (OS). A nomogram was subsequently constructed to facilitate risk assessment for individual patients. Comprehensive analyses of metabolic pathways, immune infiltration, immunomodulators, and potentially applicable drugs were conducted across different FAMRGs-related risk groups. The FAMRGs-based signature, comprising nine genes (ACOT11, APOH, BMX, CYP2R1, DPEP3, FABP6, FADS2, GLYATL2, and THRSP), demonstrated robust predictive capabilities for prognosis in The Cancer Genome Atlas (TCGA)-LUSC dataset and validated across six independent Gene Expression Omnibus (GEO)-LUSC datasets. Notably, the FAMRGs-base signature exhibited superior prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. Furthermore, the signature was closely associated with immune cell infiltration, human leukocyte antigen (HLA) genes, and immune checkpoint genes expression. Additionally, the signature demonstrated potential sensitivity to chemo-/target-therapy. The FAMRGs-based signature demonstrated superior sensitivity in predicting the prognosis of early-stage LUSC. Detecting FAMRGs may provide predictive targets for the development of clinical treatment strategies.

A Model Combining Conventional Ultrasound Characteristics, Strain Elastography and Clinicopathological Features to Predict Ki-67 Expression in Small Breast Cancer.

To establish a predictive model incorporating conventional ultrasound, strain elastography and clinicopathological features for Ki-67 expression in small breast cancer (SBC) which defined as maximum diameter less than2 cm. In this retrospective study, 165 SBC patients from our hospital were allocated to a high Ki-67 group (n = 104) and a low Ki-67 group (n = 61). Multivariate regression analysis was performed to identify independent indicators for developing predictive models. The area under the receiver operating characteristic (AUC) curve was also determined to establish the diagnostic performance of different predictive models. The corresponding sensitivities and specificities of different models at the cutoff value were compared. Conventional ultrasound parameters (spiculated margin, absence of posterior shadowing and Adler grade 2-3), strain elastic scores and clinicopathological information (HER2 positive) were significantly correlated with high expression of Ki-67 in SBC (all p < .05). Model 2, which incorporated conventional ultrasound features and strain elastic scores, yielded good diagnostic performance (AUC = 0.774) with better sensitivity than model 1, which only incorporated ultrasound characteristics (78.85%vs. 55.77%, p = .000), with specificities of 77.05% and 62.30% (p = .035), respectively. Model 3, which incorporated conventional ultrasound, strain elastography and clinicopathological features, yielded better performance (AUC = 0.853) than model 1 (AUC = 0.694) and model 2 (AUC = 0.774), and the specificity was higher than model 1 (86.89% vs. 77.05%, p = .001). The predictive model combining conventional ultrasound, strain elastic scores and clinicopathological features could improve the predictive performance of Ki-67 expression in SBC.

Combined conventional ultrasonography with clinicopathological features to predict axillary status after neoadjuvant therapy for breast cancer: A case-control study.

This study aimed to evaluate the value of a model combining conventional ultrasonography and clinicopathologic features for predicting axillary status after neoadjuvant therapy in breast cancer. This retrospective study included 329 patients with lymph node-positive who underwent neoadjuvant systemic treatment (NST) from June 2019 to March 2022. Ultrasound and clinicopathological characteristics of breast lesions and axillary lymph nodes were analyzed before and after NST. The diagnostic efficacy of ultrasound, clinicopathological characteristics, and combined model were evaluated using multivariate logistic regression and receiver operator characteristic curve (ROC) analyses. The area under ROC (AUC) for the ability of the combined model to predict the axillary pathological complete response (pCR) after NST was 0.882, that diagnostic effectiveness was significantly better than that of the clinicopathological model (AUC of 0.807) and the ultrasound feature model (AUC of 0.795). In addition, eight features were screened as independent predictors of axillary pCR, including clinical N stage, ERBB2 status, Ki-67, and after NST the maximum diameter reduction rate and margins of breast lesions, the short diameter, cortical thickness, and fatty hilum of lymph nodes. The combined model constructed from ultrasound and clinicopathological features for predicting axillary pCR has favorable diagnostic results, which allowed more accurate identification of BC patients who had received axillary pCR after NST. A combined model incorporated ultrasound and clinicopathological characteristics of breast lesions and axillary lymph nodes demonstrated favorable performance in evaluating axillary pCR preoperatively and non-invasively.

Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients

BackgroundTumor antigenicity and efficiency of antigen presentation jointly influence tumor immunogenicity, which largely determines the effectiveness of immune checkpoint blockade (ICB). However, the role of altered antigen processing and presentation machinery (APM) in breast cancer (BRCA) has not been fully elucidated. MethodsA series of bioinformatic analyses and machine learning strategies were performed to construct APM-related gene signatures to guide personalized treatment for BRCA patients. A single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) were combined to screen for BRCA-specific APM-related genes. The non-negative matrix factorization (NMF) algorithm was used to divide the cohort into different clusters and the fgsea algorithm was applied to investigate the altered signaling pathways. Random survival forest (RSF) and the least absolute shrinkage and selection operator (Lasso) Cox regression analysis were combined to construct an APM-related risk score (APMrs) signature to predict overall survival. Furthermore, a nomogram and decision tree were generated to improve predictive accuracy and risk stratification for individual patients. Based on Tumor Immune Dysfunction and Exclusion (TIDE) method, random forest (RF) and Lasso logistic regression model were combined to establish an APM-related immunotherapeutic response score (APMis). Finally, immune infiltration, immunomodulators, mutational patterns, and potentially applicable drugs were comprehensively analyzed in different APM-related risk groups. IHC staining was used to assess the expression of APM-related genes in clinical samples. ResultsIn this study, APMrs and APMis showed favorable performances in risk stratification and therapeutic prediction for BRCA patients. APMrs exhibited more powerful prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. APMrs was closely associated with distinct mutational patterns, immune cell infiltration and immunomodulators expression. Furthermore, the two APM-related gene signatures were independently validated in external cohorts with prognosis or immunotherapeutic responses. Potential applicable drugs and targets were mined in the APMrs-high group. APM-related genes were further validated in our in-house samples. ConclusionThe APM-related gene signatures established in our study could improve the personalized assessment of survival risk and guide ICB decision-making for BRCA patients.

Establishing a prognostic model with ferroptosis-related long non-coding RNAs in bladder cancer.

Ferroptosis is a distinct form of cell death that has the potential to supersede the drug resistance that is commonly observed with current chemotherapeutic agents. As a result, ferroptosis presents a new and innovative therapeutic pathway for cancer treatment. The current understanding regarding the expression of genes associated with ferroptosis in bladder cancer (BLCA) and their prognostic implications remains unclear. Consequently, this study aimed to examine the potential prognostic value of ferroptosis-associated long non-coding RNAs (lncRNAs) in BLCA. The Cancer Genome Atlas (TCGA) was accessed to download RNA sequencing data and clinicopathological features of BLCA while accessing the FerrDb database to download ferroptosis-associated genes. The study calculated risk scores for ferroptosis-associated lncRNAs, and subsequently divided patients with BLCA into two groups, namely high- and low-risk, on the basis of the median risk score. Moreover, Kaplan-Meier (K-M) curves, Cox regression analysis, and column plots were utilized for evaluating the risk score prognostic value. Subsequently, the involvement of ferroptosis-associated mRNA, N6-methyladenosine (m6A) mRNA status, and immune responses was investigated for BLCA prognosis. Thirty-six lncRNAs were identified to be differently expressed and linked to the prognosis of BLCA. The findings from the K-M curve analysis indicated a significant association between a high-risk lncRNA profile and poor BLCA prognosis. The area under curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.810. The risk assessment model exhibited superior performance in predicting prognosis for BLCA compared to conventional clinicopathological features. Thirty-six lncRNAs were found to be linked to ferroptosis for the prognosis of patients with BLCA, and these results may provide new insights for treating BLCA.

Clinicopathologic Features of Noninvasive Inverted Urothelial Papillary Tumor.

Clinicopathologic features and recurrence rates of inverted noninvasive urothelial papillary tumors have been poorly characterized to date with few larger studies evaluating long-term outcomes. The spectrum of histomorphology, clinical features, and prognosis of inverted lesions of the urinary bladder are retrospectively reviewed. Archived paraffin-embedded urothelial tumor samples from patients diagnosed with inverted urothelial papillary lesions between January 2005 and June 2020 were collated. A matched control population of patients with exophytic papillary lesions of the urothelium diagnosed during the same time period was randomly selected. The conventional clinicopathologic features of inverted urothelial papillary tumor were evaluated retrospectively and patient demographics, tumor characteristics, recurrence, and survival information were recorded. Lower recurrence rates were observed for inverted papillary urothelial neoplasm of low malignant potential (IPUNLMP) relative to papillary urothelial neoplasms of low malignant potential and for low-grade papillary urothelial carcinoma with an inverted growth pattern (LG-PUCI) relative to low-grade papillary urothelial carcinomas. No recurrence was found among the inverted urothelial papilloma cases. The 2- and 5-year disease-free survival rates were 100.0% and 85.2% for IPUNLMP patients; 94.4% and 80.4% for papillary urothelial neoplasms of low malignant potential; 89.5% and 82.0% for LG-PUCI; 73.7% and 54.6% for low-grade papillary urothelial carcinoma; 40.0% and 20.0% for high-grade papillary urothelial carcinoma with an inverted growth pattern patients and 26.7% and 26.7% for high-grade papillary urothelial carcinoma. Multivariate Cox regression analysis of IPUNLMP and LG-PUCI/high-grade papillary urothelial carcinoma with an inverted growth pattern indicated that tumor number (hazard ratio=4.356; 95% CI: 1.145-16.570; P =0.031) was a powerful prognostic factor for disease-free survival. Noninvasive, papillary urothelial lesions of the bladder tend to have lower recurrence and a better outcome if an inverted growth pattern is shown.

Inflammation-based prognostic scores in geriatric patients with rectal cancer.

Morbidity/mortality and oncologic outcomes can be worsened in geriatric rectal cancer patients due to comorbidities and frailty. The aim of this study was to compare surgical and oncological results of geriatric rectal cancer patients using inflammation-based prognostic scores. The prospectively maintained database of 991 rectal cancer patients treated at our center between 2007 and 2020 were analyzed. All conventional clinicopathologic features, and oncologic outcomes are compared between patients ≥ 65years old (geriatric patients: Group I) and < 65years old (non-geriatric patients: Group II). The modified Glasgow Prognostic Score (mGPS) and the C-reactive protein-albumin ratio (CAR), were determined. The prognostic value of mGPS and CAR as well as the well-known clinico-pathologic factors to predict surgical morbidity, mortality, local and/or distant recurrence, and overall survival was assessed. There were 567 (57.2%) patients who were ≥ 65years old (Group I; 349 males, median age 74 [range 65-9]) years) and 424 (42.8%) who were < 65years old (Group II; 252 males, median age 58 [range 20-64] years). The high-grade [Clavien-Dindo III-IV] complications rates of Group I and Group II patients sere 20% (n = 113), and 9% (n = 37), respectively. High-grade complications were related to mGPS (p < 0.001) and CAR (p < 0.001) values. The high-grade complication rate was found to be higher in Group I than in Group II, and this was statistically significant (p < 0.001). High preoperative mGPS and CAR values were significantly associated with postoperative mortality (p < 0.001). In Cox multivariate analysis, mGPS (p = 0.003) and CAR (p = 0.001) were significantly in correlation with lowered overall survival. The mGPS and CAR were found to be independent prognostic factors for overall survival. The mGPS and CAR can predict severe postoperative complications and early mortality. mGPS, and CAR have a powerful prognostic value and the potential clinical usefulness to predict decreased overall survival in both geriatric and non-geriatric rectal cancer patients.

Immunization Combined with Ferroptosis Related Genes to Construct a New Prognostic Model for Head and Neck Squamous Cell Carcinoma.

Simple SummaryImmunity combined with ferroptosis is being considered as a new tumor treatment modality, and its regulation in head and neck squamous cell carcinoma is still unknown. The purpose of this study was to look into the potential molecular biological roles of immune ferroptosis genes in head and neck squamous cell carcinoma. The 12-IFRM signatures were successfully constructed and classified into high- and low-risk groups using the TCGA database and related data resources. In patients with head and neck squamous cell carcinoma, feature-based risk scores were more predictive of survival than traditional clinicopathological features. Furthermore, the expression of CD8+T cells and macrophage M0 differed significantly between the two groups. The expression of TNFSF9 and CD44 in the high-risk groups was significantly increased compared with the low-risk groups. Next, we found a higher proportion of high-risk mutations than in the low-risk group. In addition, the high-risk group was more sensitive to some chemotherapy drugs. Finally, we performed correlation analysis on the model genes. In this paper, the 12-IFRM signatures was developed with promising application prospects for predicting the clinical outcomes and treatment outcomes in head and neck squamous cell carcinoma.Ferroptosis is a new type of programmed cell death that plays a pivotal role in a variety of tumors. Moreover, immunity is closely related to ferroptosis. However, immune-ferroptosis-related mRNAs (IFRMs) are still not fully understood in the regulation of head and neck squamous cell carcinoma (HNSC). The purpose of this paper was to investigate the IFRMs prediction of HNSC and its possible molecular biological role. RNA-Seq and related clinical data were mined from the TCGA database, ImmPort database, GeneCards database, FerrDb database, and previous data. In R software, the “DESeq2” package was used to analyze the differential expression of IFRMs. We used univariate Cox analysis to judge the prognosis of the IFRMs. Using the least absolute shrinkage and selection operator (LASSO) and Cox regression, a prediction model for 12 IFRMs was established. In this study, the Kaplan–Meier survival curve and receiver operating characteristic (ROC) curve analysis were used to evaluate the prediction results. Moreover, factors such as immune landscape, somatic mutations, and drug susceptibility are also discussed. We successfully constructed the signature of 12-IFRMs. The two risk groups were classified according to the risk score obtained by this signature. Compared with conventional clinicopathological features, the characteristic-based risk score was more predictive of survival in patients with HNSC. Furthermore, the expression of CD8+T cells and macrophage M0 differed significantly between the two groups. Moreover, the expression of TNFSF9 and CD44 in high-risk groups was significantly increased compared with the low-risk groups. Then, we found a higher proportion of high-risk mutations than in the low-risk group. Next, the high-risk group was more sensitive to chemotherapy drugs such as bosutinib, docetaxel, erlotinib, gefitinib, imatinib, lapatinib, and sorafenib. Finally, an in-depth analysis of the association and potential value of the 12 genes was performed. In summary, the 12-IFRM signatures established in this paper had good application prospects and could be effectively used to predict the clinical outcome and treatment response of head and neck squamous cell carcinoma.

Analysis of non-severe acute onset autoimmune hepatitis according to the presence of radiological heterogeneity.

Diagnosis of acute onset autoimmune hepatitis (A-AIH) has been difficult in that patients may not have typical clinicopathological features of AIH. In our previous reports of severe and fulminant AIH, two-thirds of them showed radiological heterogeneity: hepatic heterogeneous hypoattenuation on unenhanced computed tomography (CT) reflecting heterogeneous distribution of massive hepatic necrosis (severe centrilobular necrosis), which would be beneficial for the diagnosis. In the present study, we analyzed non-severe A-AIH patients with or without radiological heterogeneity and tried to find novel clinical features for supporting the early diagnosis. Clinical, biochemical, immunological, radiological and histological features of 42 patients with non-severe A-AIH at community hospitals between 2010 and 2020 were analyzed. Of 42, 28 patients on whom CT scans were performed and who could be fully analyzed were enrolled. Five patients showed hepatic heterogeneous hypoattenuation on unenhanced CT. There was no significant difference in clinical, biochemical, immunological and histological features at diagnosis between the two groups according to the presence of radiological heterogeneity, although mean minimum prothrombin time activity during the course was lower in patients with heterogeneity without statistical significance (p=0.080). All responded to treatment well and achieved initial remission within 3months. It is possible that patients with non-severe A-AIH show radiological heterogeneity reflecting centrilobular necrosis which is one of important diagnostic features of A-AIH. Accordingly, radiological heterogeneity might be beneficial for the diagnosis of A-AIH in combination with conventional clinicopathological features if it is detected in the absence of features suggestive of other liver diseases.

A Prognostic Model of Differentiated Thyroid Cancer Based on Up-Regulated Glycolysis-Related Genes

ObjectiveThis study aims to identify reliable prognostic biomarkers for differentiated thyroid cancer (DTC) based on glycolysis-related genes (GRGs), and to construct a glycolysis-related gene model for predicting the prognosis of DTC patients.MethodsWe retrospectively analyzed the transcriptomic profiles and clinical parameters of 838 thyroid cancer patients from 6 public datasets. Single factor Cox proportional risk regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) were applied to screen genes related to prognosis based on 2528 GRGs. Then, an optimal prognostic model was developed as well as evaluated by Kaplan-Meier and ROC curves. In addition, the underlying molecular mechanisms in different risk subgroups were also explored via The Cancer Genome Atlas (TCGA) Pan-Cancer study.ResultsThe glycolysis risk score (GRS) outperformed conventional clinicopathological features for recurrence-free survival prediction. The GRS model identified four candidate genes (ADM, MKI67, CD44 and TYMS), and an accurate predictive model of relapse in DTC patients was established that was highly correlated with prognosis (AUC of 0.767). In vitro assays revealed that high expression of those genes increased DTC cancer cell viability and invasion. Functional enrichment analysis indicated that these signature GRGs are involved in remodelling the tumour microenvironment, which has been demonstrated in pan-cancers. Finally, we generated an integrated decision tree and nomogram based on the GRS model and clinicopathological features to optimize risk stratification (AUC of the composite model was 0.815).ConclusionsThe GRG signature-based predictive model may help clinicians provide a prognosis for DTC patients with a high risk of recurrence after surgery and provide further personalized treatment to decrease the chance of relapse.

Can miRNAs be useful biomarkers in improving prognostic stratification in endometrial cancer patients? An update review.

Endometrial cancer (EC) is the most common gynecological cancer, with annual incidence rates in Western countries ranging between 15 and 25 per 100 000 women. About 15% to 20% of patients with EC have high‐risk disease and follow an aggressive clinical course. Unfortunately, the assessment of histologic parameters is poorly reproducible and conventional clinicopathological and molecular features do not reliably predict either the patient's response to the available treatments or the definition of personalized therapeutic approaches. In this context, the identification of novel diagnostic and prognostic biomarkers, which can be integrated in the current classification schemes, represents an unmet clinical need and an important challenge. miRNAs are key players in cancer by regulating the expression of specific target genes. Their role in EC, in association with clinical and prognostic tumor biomarkers, has been investigated but, so far, with little consensus among the studies. The present review aims to describe the recent advances in miRNAs research in EC taking into consideration the current classification schemes and to highlight the most promising miRNAs. Finally, a perspective point of view sheds light on the challenges ahead in the landscape of EC.

A Risk Signature with Autophagy-Related Long Noncoding RNAs for Predicting the Prognosis of Clear Cell Renal Cell Carcinoma: Based on the TCGA Database and Bioinformatics.

Background Disorders of autophagic processes have been reported to affect the survival outcome of clear cell renal cell carcinoma (ccRCC) patients. The purpose of our study was to identify and validate the candidate prognostic long noncoding RNA signature of autophagy. Methods Transcriptome profiles were obtained from The Cancer Genome Atlas. The autophagy gene list was obtained from the Human Autophagy Database. Based on coexpression analysis, we obtained a list of autophagy-related lncRNAs (ARlncRNAs). GO enrichment analysis and KEGG pathway analysis were conducted to explore the functional annotation of these ARlncRNAs. Univariate and multivariate Cox regression analyses were conducted to elucidate the correlation between overall survival and the expression level of each ARlncRNAs. We then established a prognostic signature that was a linear combination of the regression coefficients from the multivariate Cox regression model (β) multiplied by the expression levels of the respective ARlncRNAs in the training cohort. The predictive performance was tested in the validation cohort. Additionally, the independence of the risk signature was assessed, and the relationship between the risk signature and conventional clinicopathological features was explored. Results Seven autophagy-related lncRNAs with prognostic value (SNHG3, SNHG17, MELTF-AS1, HOTAIRM1, EPB41L4A-DT, AP003352.1, and AC145423.2) were identified and integrated into a risk signature, dividing patients into low-risk and high-risk groups. The risk signature was independent of conventional clinical characteristics as a prognostic indicator of ccRCC (HR, 1.074, 95% confidence interval: 1.036-1.113, p < 0.001) and was valuable in the prediction of ccRCC progression. Conclusion Our risk signature has potential prognostic value in ccRCC, and these ARlncRNAs may play a significant role in ccRCC tumor biology.

Characterization of immune landscape in papillary thyroid cancer reveals distinct tumor immunogenicity and implications for immunotherapy

ABSTRACT Although the vast majority of patients with papillary thyroid cancer (PTC) have a favorable prognosis when conventional treatments are implemented, local recurrence and distant metastasis of advanced PTCs still hamper the survival and clinical management in certain patients. As immune checkpoint blockade (ICB) therapy achieves a great success in some advanced cancers, we aimed to investigate the immune landscape in PTC and its potential implications for prognosis and immunotherapy. In this study, different algorithms were conducted to estimate immune infiltration in PTC samples. A series of bioinformatic and machine learning approaches were performed to identify PTC-specific immune-related genes (IRGs) and distinct immune clusters. Differences in intrinsic tumor immunogenicity and potential immunotherapy response were observed between distinct immune clusters. A prognostic immune-related signature (IRS) was established to predict progression-free survival (PFS). IRS exhibited more powerful prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. Furthermore, an integrated survival decision tree and a scoring nomogram were constructed to improve prognostic stratification and predictive accuracy for individual patients. In addition, altered pathways, mutational patterns, and potential applicable drugs were analyzed in different immune-related risk groups. Our study gained some insight into the immune landscape of PTC, and provided some useful clues for introducing immune-based molecular classification into risk stratification and guiding ICB decision-making.

Analysis of Tumor Genomic Pathway Alterations Using Broad-Panel Next-Generation Sequencing in Surgically Resected Lung Adenocarcinoma.

The majority of broad-panel tumor genomic profiling has used a gene-centric approach, although much of that data is unused in clinical decision making. We hypothesized that a pathway-centric approach using next-generation sequencing (NGS), combined with conventional clinicopathologic features, may better predict disease-free survival (DFS) in early stage lung adenocarcinoma. Utilizing our prospectively maintained database, we analyzed 492 patients with primary, untreated, completely surgically resected lung adenocarcinoma. Ten canonical pathways were analyzed using broad-panel NGS. The correlations of DFS and number (and type) of pathway (NPA) were analyzed using the Kaplan-Meier method and log-rank test. Associations between altered pathways and clinicopathologic variables, as well as identification of actionable therapeutic strategies were explored. Median NPA for the cohort was two (range, 0-5). Smoking status, solid morphologic appearance on preoperative CT, maximal standardized uptake value, pathologic tumor size, aggressive histologic subtype, lymphovascular invasion, visceral pleural invasion, and positive lymph nodes were significantly associated with NPA (P < 0.05). Of 543 actionable genetic alterations identified, 455 (84%) were within the RTK/RAS pathway. A total of 86 tumors had actionable therapeutic genomic alterations in >1 pathway. On multivariable analysis, higher NPA was significantly associated with worse DFS (HR, 1.31; P = 0.014). NPA and specific pathway alterations are associated with clinicopathologic features in patients with surgically resected lung adenocarcinoma. Cell cycle, Hippo, TGFβ, and p53 pathway alterations are associated with poor DFS. Finally, NPA is an independent risk factor for poor DFS in our cohort.See related commentary by Blakely, p. 7269.

A prognostic eight-lncRNA expression signature in predicting recurrence of ER-positive breast cancer receiving endocrine therapy.

Long noncoding RNAs (lncRNAs) have the main role in the tumorigenesis of breast cancer. In the present study, lncRNA expression profiling was collected to identify a lncRNA expression signature from the Gene Expression Omnibus database. An eight-lncRNA signature was established to predict the survival of patients with estrogen receptor (ER)-positive breast cancer receiving endocrine therapy. Patients were separated into a low-risk group and a high-risk group based on this signature. Patients in high-risk group have worse survival compared to those in low-risk group using Kaplan-Meier curve analysis with log-rank test. Receiver operating characteristic analysis suggested good diagnostic efficiency of the eight-lncRNA signature. When adjusting the clinical features, including age, grade, lymph node status, and tumor size, this signature was independently associated with the relapse-free survival. The prognostic value of the lncRNA prognostic model was then validated in validation sets. When validated in a cohort of patients treated with neoadjuvant chemotherapy and endocrine therapy, this signature demonstrated good performance as well. Besides, we have built a nomogram that integrated the conventional clinicopathological features and the eight-lncRNA-based signature. To sum up, our results indicated that the eight-lncRNA prognostic model was a reliable tool to group patients at high and low risk of disease relapse. This signature may have possible implication in prognostic evaluations of patients with ER-positive breast cancer receiving endocrine therapy.

Evaluation of the prognostic value of paraoxonase 1 in the recurrence and metastasis of hepatocellular carcinoma and establishment of a liver-specific predictive model of survival

BackgroundHepatocellular carcinoma is a malignant tumor with a highly invasive and metastatic phenotype, and the detection of potential indicators associated with its recurrence and metastasis after surgical resection is critical for patient survival.MethodsTranscriptome data for large cohorts (n = 1432) from multicenter sources were comprehensively analyzed to explore such potential signatures. The prognostic value of the selected indicators was investigated and discussed, and a comparison with conventional clinicopathological features was performed. A survival predictive nomogram for 5-year survival was established with the selected indicator using the Cox proportional hazards regression. To validate the indicator at the protein level, we performed immunohistochemical staining with paraffin-embedded slides of hepatocellular carcinoma samples (n = 67 patients) from our hospital. Finally, a gene set enrichment analysis (GSEA) was performed to detect the underlying biological processes and internal mechanisms.ResultsThe liver-specific protein paraoxonase 1 (PON1) was found to be the most relevant indicator of tumor recurrence, invasiveness, and metastasis in the present study, and the downregulation of PON1 might reveal poor survival for patients with hepatocellular carcinoma. The C-index of the PON1-related nomogram was 0.714, thus indicating a more effective predictive performance than the 7th American Joint Committee on Cancer (AJCC) tumor stage (0.534), AJCC T stage (0.565), or alpha-fetoprotein (0.488). The GSEA revealed that PON1 was associated with several hepatocellular carcinoma-related pathways, including the cell cycle, DNA replication, gap junction and p53 downstream pathways.ConclusionsThe downregulation of paraoxonase 1 may suggest worse outcomes and a higher recurrence rate. Thus, paraoxonase 1 might represent an indicator for predicting the survival of patients with hepatocellular carcinoma.

A three-molecule score based on Notch pathway predicts poor prognosis in non-metastasis clear cell renal cell carcinoma

We constructed a three-molecule score based on the expression of Notch pathway molecules: Jagged1, intracellular Notch1 (ICN1) and Hes1 (JIH score). To assess prognostic value of the JIH score in non-metastasis clear cell renal cell carcinoma (ccRCC), we identified 467 patients who underwent nephrectomy during 2008-2009 as our study population. Immunohistochemistry was used to evaluate the expression of these three molecules. Cox regression models were applied to construct the JIH score, while Kaplan-Meier methods, multivariate analyses and nomogram were used to explore prognostic value of the JIH score. Our result confirmed that JIH score was an independent prognosticator for both overall survival (OS) and recurrence-free survival (RFS). Survival analyses showed that a higher JIH score indicated worse clinical outcomes (JIH score 3: 58.3% and 58.0% for 6-year OS and RFS, respectively; JIH score 0: 96.7% and 91.6% for 6-year OS and RFS, respectively). Nomograms based on JIH score and other conventional clinicopathological features had a better capability in predicting patients with pT1 stage disease for both OS and RFS (84.6% and 83.9%, respectively). The JIH score is a novel prognosticator representing activation of Notch pathway for non-metastasis ccRCC, and raises an alternative strategy for excavating potential biomarkers for signal pathways.

Development of triple-negative breast cancer radiosensitive gene signature and validation based on transcriptome analysis.

Triple-negative breast cancer (TNBC) is a heterogeneous disease with highest loco-regional recurrence among breast cancer subtypes. Radiotherapy is indispensable for TNBC loco-regional control. However, intrinsic radiosensitivity differences exist in TNBC patients and RT is still prescribed mainly based on conventional clinicopathologic features of patients without considering the differences. The purpose of the present study is to develop and validate a TNBC radiosensitive gene signature (RSGS) and to guide therapeutic decisions. In this study, we compared transcriptome profiles of 12 locally recurrent TNBCs to 20 non-locally recurrent TNBCs treated with surgery radio-chemotherapy and developed a seven-gene RSGS and a simplified three-gene RSGS by using pathway analysis, univariate Cox proportional hazards regression model and rank-based linear algorithm. They were validated by using transcriptome profiles of 166 TNBC patients. Two gene signatures specifically identified a radiosensitive population that had an improved recurrence-free survival in patients treated with surgery radio-chemotherapy (Radiosensitive patients vs radioresistant patients, for seven-gene RSGS: P = 0.024, HR = 0.35, 95 %CI 0.14–0.87 and for three-gene RSGS: P = 0.035, HR = 0.38, 95 %CI 0.15–0.94). In contrast, there was no significant difference in outcome between predicted radiosensitive and radioresistant patients that treated with other treatment modality. RSGSs provide a useful tool for identification of radiosensitive/radioresistant TNBC patients and they could lead to a better selection of patients for RT protocols.